110 L. D. Martin et al.


endemic mouse lemurs of Madagascar (Cheirogaleidae: Microcebus; Louis et al., 2008; Groves, 2016; Schüßler et al., 2020). The genus was long considered to comprise just two species, one from the dry forests of the west and south and one from the rainforests of the east (Groves, 2005), but today 25 species are recognized. One consequence of these taxonomic revisions is that the geographical distri- butions and conservation status of new species, and those from which they are distinguished, must be reassessed (Louis et al., 2006; Rasoloarison et al., 2013), as splitting species often results in proportionately more threatened species with reduced ranges (Agapow et al., 2004). Several newly described mouse lemur species, however, have not been surveyed and population data are incomplete or absent (Setash et al., 2017;Hending, 2021). Population estimates are needed to support IUCN Red List assessments, to allow conservation managers to identify, prioritize and monitor vulnerable species and populations, and to help evaluate conservation programmes (Plumptre & Cox, 2006; Kühl et al., 2008; Rylands et al., 2008, 2020). Claire’s mouse lemur Microcebus mamiratra was first


described in 2006 (Andriantompohavana et al., 2006) and has been the subject of limited field research (Hasiniaina et al., 2018; Webber et al., 2020; Tinsman et al., 2022). Its geographical range is confined to the humid primary and secondary forests of the Lokobe National Park region on the island of Nosy Be in north-west Madagascar, as well as some small, isolated humid forest fragments on the Malagasy mainland near Manehoka and Ambakirano, east of Nosy Be (Olivieri et al., 2007; Sgarlata et al., 2019; Blanco et al., 2020). Threatened mainly by habitat loss and degradation, it is categorized as Endangered on the IUCN Red List (Blanco et al., 2020). Lemurs have been ex- tirpated from small islands elsewhere in Madagascar, and the population of M. mamiratra on Nosy Be is at risk (Goodman, 1993; Hyde Roberts & Daly, 2014). No system- atic surveys have been carried out anywhere across its re- stricted and severely fragmented range, and there are no population size or density estimates (Blanco et al., 2020). Although encounter rates have been reported for M. ma- miratra (Tinsman et al., 2022), these provide only a relative abundance index and could be affected by differences in detection probability, such as those between observers or environmental variables (Anderson, 2001; Buckland et al., 2008; Campbell et al., 2016). Distance sampling is a powerful method for estimating


absolute population density (number of individuals per unit area) and population size (density multiplied by area). It comprises a set of standardized survey techniques, principally line transects and point transects, in which ob- servers record distances to detected objects whilst traversing lines or standing at points that are placed randomly within a survey area (Buckland et al., 1993, 2001, 2004). The detected objects are usually animals of the target species but might be


animal cues (e.g. calls) or signs (e.g. nests). Intuitively, we expect that objects become more difficult to detect with in- creasing distance from the line or point and that some ob- jects might be missed.Akey strength of distance sampling is that it accounts for imperfect detection: the distribution of observed distances is used to model a detection function that describes the probability that an object is detected as a function of distance from the line or point, thereby allow- ing estimation of the proportion of objects missed during the surveys (Buckland et al., 1993, 2001, 2004). This can be particularly advantageous for animals that are otherwise dif- ficult to detect, such as small-bodied and nocturnal mouse lemurs (Meyler et al., 2012; Schäffler & Kappeler, 2014). In primatology, line transects are the most popular form of dis- tance sampling (Plumptre, 2000; Ross & Reeve, 2003; Plumptre et al., 2013). Proper inference in line transect dis- tance sampling relies on the following key assumptions: (1) objects directly on the line are detected with certainty, (2) objects are detected at their initial location, prior to any movement in response to the observer, and (3) distances to detected objects are measured accurately (Buckland et al., 1993, 2001, 2004; Buckland, 2006; Plumptre et al., 2013). A further assumption, relevant for group-living primates, is (4) group sizes (or clusters) are accurately recorded (Buckland et al., 2010). Mouse lemurs are amenable to line transect surveys for several reasons: (1) their tapetum luci- dum (reflective eye tissue) and preference for the under- storey aid detection on the centreline (Lahann, 2008; Rakotondravony &Radespiel, 2009), (2) they move relative- ly slowly and often become stationary when observed (Meyler et al., 2012), (3) they are mostly solitary, generally removing the need to estimate cluster size and spread (Buckland et al., 2010; Bessone et al., 2023), and (4) they are generally abundant, so adequate samples sizes can be ob- tained (Kappeler & Rasoloarison, 2003). Line transect dis- tance sampling also relies on two underlying principles of survey design: (1) randomization (i.e. the lines should be placed randomly, and not subjectively, in the survey area; e.g. systematically spaced parallel lines with a random start point; Marques et al., 2010; Thomas et al., 2010; Hilário et al., 2012), and (2) replication (i.e. an adequate number of lines (at least 10–20) should be placed; Buckland et al., 1993, 2001, 2004; Thomas et al., 2010). In practice, however, surveys are often compromised by time and resource constraints (e.g. rapid assessments), and the key assumptions are routinely violated or cannot be met (Buckland et al., 2010; de Andrade et al., 2019). Recent meta-analyses of published density estimates of mouse le- murs have highlighted that non-standardized designs (e.g. using non-random established trails as transects) and ana- lysis methods (e.g. not accounting for detection probability) are prevalent (Setash et al., 2017; Hending, 2021). Poor sur- vey practices can bias results and inhibit rigorous inference; at worst, they can lead to incorrect conservation status


Oryx, 2025, 59(1), 109–118 © The Author(s), 2025. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605324000772


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